A MOBILE-TO-GRID GATEWAY MODEL AND LOAD SCHEDULING SCHEMEFOR e-HEALTH SERVICE IN GRID

Youngjoo Han, Chan-Hyun Youn, Hyewon SongSchool of Engineering, Information and Communications University

119, Munjiro, Yuseong-gu, Daejon, 305-732, Korea{yjhan, chyoun, hwsong}@icu.ac.kr

AbstractBecause ofthe huge amounts ofdata set receivedfrom thenumerous wireless devices in specific e-Health service, themission-critical application in particular e-Health servicesuch as ECG application and Carotid artery applicationnormally demands the highly sophisticated handlingmethod and reliable processing scheme in wireless Gridenvironments. Thus we propose the refined proceduresbased on wireless Grid to guarantee the optimalprocessing time and reliability as prime factor of thesystem performance by using the proposed schedulingscheme. The simulation results show that proposed loadscheduling can support the load balancing and guaranteethe high system availability.

1. INTRODUCTION

Grid computing brings together all heterogeneousresources and allocates them efficiently to applications.Currently, the studies of Grid technology with wirelesstechnology including mobile devices, wirelessapplications, and quality of services (Qos), have beenevolving rapidly as one of the most significant researchareas. Grid could be an important solution as a serviceprovider that enables mobile users to perform somecomplicated jobs within various conditions becausemobile devices have too limited computing capacity todeal with large amounts of data or compute process withhigh complexity. For that reason, we propose the servicenamed "Mobile-to-Grid Services" (MGS) in this paper.Furthermore, the e-Health services could be considered asa main application for MGS for verification ofperformance factors like availability and stability.

The paradigm of load distributions is basicallyconcerned with a single large load which originates orarrives at one of the mobile gateway in wireless Grid. Theload is massive and requires an enormous amount of timeto process given the computing capability of the mobiledevice. The mobile gateway partitions the load into manyfractions, and then sends the entire fraction to gridresource in wireless Grid for processing. An importantproblem here is to decide how to archive a balance in theload distribution between mobile-to-grid gateway and grid

resource so that the computation is completed in theshortest possible time. In this time, the load balancingissues are related to both deciding the size of load toprocess on each resource and load scheduling. Thisbalancing can be done at the beginning or dynamically asthe computation progresses.

In this paper, we propose architecture of mobile-to-Gridgateway for supporting two kind of e-Health application:ECG Application and Carotid artery application. Also, wesuggest the divisible and indivisible load balancingmechanism to find optimal finish time in wireless Grid.Furthermore, we will prove the effectiveness of loadbalancing and improvement of availability with proposedload scheduling algorithm in mobile-to-grid gatewaywithin the given analytical model for reflecting ofadvantages through combining mobile and Grid.

2. RELATED WORK

The paper [1] showed how network resources'availability and connectivity must be coordinated betweenwireless and Grid networks. Their proposed solution, theproxy based system model, interacts with Grid discoveryservices to communicate with available resources in thenetworks. However, for the reason of low power, if mobiledevice moves to the place where the wireless networksignal is weak, the connection between the mobile deviceand the proxy would be disconnected obviously. Thus, themobile device can not receive the result of job requestalthough the mobile device's disconnection may betemporary. This kind of unpredictable disconnection iscommon in the wireless mobile computing. Therefore,there is a need to store the result of the job requesttemporarily and send it again to the mobile device whenthe network condition is getting better.

Mediation [2] is the process of integrating disparateinformation sources in a timely fashion. Mediationprovides a single interface to the different networkelements implied in a communication, thus reducing thenumber of connections and facilitating changemanagement with regard to the way data is transmitted ortransformed. Mediation also provides an isolation layerfrom hardware and software details.

1 of 7

Factors to consider when selecting a machine forprocess execution include resource availability andoptimum use of resources. In a distributed computingenvironment, a load balancing algorithm seeks the leastbusy machine. At the same time, the load balancingalgorithm must not overload the system. Ideally, the loadbalancing algorithm selects the machine for processexecution based on available information. In case of theWRRLB, loads are distributed in a weighted cyclical orderaccording to system utility. The RLB supports that loads(tasks) are scheduled at a particular node with probabilityp. However, these approaches are only focus on processor(CPU) availability to estimate the system utilization.

Divisible load theory (well-know for DLT [3]) is amethodology involving the linear and continuousmodeling of partitionable computation and communicationloads for parallel processing. It adequately represents animportant class of problems with applications in paralleland distributed system scheduling, various types of dataprocessing, scientific and engineering computation andsensor networks. However, limited assumption regardingtype of load, such that load should be distributed inarbitrary manner, is not applicable to Grid application aptto include invisible or modularly divisible data.

Therefore, we introduce scalable load balancing schemewith consideration of system status such as CPU andbandwidth, because the amount of work done by the loadbalancing algorithm may significantly increase systemperformance.

3. MODEL DESCRIPTION

3.1. Handheld based Medical Grid Service

Handheld computing based medical grid application inour research is considered as 2 applications: ECGApplication (Fig. 1 (b)) and Carotid artery application(Fig.1 (b)). As depicted in Fig. 1, it shows service scenarioof e-Health Grid application. An electrocardiogrammeasures the patient's ECG data, and then the data isanalyzed over the grid environment. So the patients can beplaced under hospital-medical care in their house easily.For Carotid artery application, in case the general hospitalis in the far distance, the patients have their Carotid arteryimage taken in near a public health center. The image alsocan be analyzed over the grid environment and finally thepatients receive more valuable medical attention.

In ECG application, each patient's data is measuredcontinuously and as time passes, the amount of data mightbe too big to handle using only mobile devices or insimple host environment. Also, in order to make anaccurate diagnosis of patient's health, it is requisite to referto not only resent ECG data but huge history ECG data.

As a necessary consequence, it requires much morecomputing power than we expect. Also in case processinga patient's image from portable ultrasonograph, it isessential to have powerful computing process. In the event,utilizing grid resources might be core technology logically.

:

-ha rri N .kqlwLrt.HtlP

(a) e-Health Service Scenario

(c) Carotid artery applicationFig. 1 wireless Medical Grid

3.2. Mobile-to-Grid Gatewayfor supporting services

A. Mobile-to-Grid Gateway (MG)Since the mobile device has been suffering from the

intermittent connectivity, its availability is unreliable. So,it is not easy to submit a job or monitor jobs sent to Griddirectly and efficiently. In order to solve this problem, wepropose to use Mobile-to-Grid Gateway as a middlewareservice as shown in Fig. 2. In a simple Mobile-to-Grid

2 of 7

service scenario, in which a mobile user can submit jobs toGrid via MG, the MG after receiving jobs will perform asa scheduler and manager according to the connectionstatus of mobile user. If a lot of devices send a job to MG,it should separate the load for guaranteeing minimumprocessing time. In addition, if the MG finds mobiledevices disconnected, it will become operational and startmonitoring jobs which might be pending due tounforeseen exceptions on Grid sites. And if someinteractions between the executing job and mobile deviceare needed, such as input variables or conditions, the MGwill perform the interaction on behalf of the mobile user,based on the information received from the submitted jobout of mobile user beforehand. When the Grid job iscompleted, the result is temporarily stored on the MG.Then mobile device comes back and has a good conditionto receive the results, MG will immediately relay theresult data. Otherwise, MG should wait until the mobiledevice becomes ready to receive the result.

the mobile device that it represents and that both entitiesare active. When the MG loses the connection, the MGkeep the result till the device reconnects to MG.Replication manager serves high reliability service. Weconsider that a grid system provides a critical e-healthservice to mobile devices (such as electrocardiogram andcarotid artery), which requires a very high reliabilityespecially in emergent situation. In this case, it is veryunsafe if we store only one data. Since host which has auser medical data can be broken down, or overloaded dueto some reasons, the mobile user has no way to recover aswell as get results from his submitted jobs. Though thereplica manager, MG can guarantee the reliable service. Ine-Health application, it is very important to control accessto private health data because one user's data should beaccessed by oneself or their doctors. Access managercontrol the access authority based on metadata. Lastly,load scheduler provides optimal scheduling for divisiblejob and indivisible job and has an effect on load balancingby using proposed algorithm. In section 3.3 and 3.4, wewill discuss load scheduling scheme.

Mobile Grid Application

Electro Cardiogram Carotid artery stenosisAnalysis / Store Analysis / Store

N)

Fig. 2 Architecture of Medical Grid Application

B. Architecture of Mobile-to-Grid GatewayOur MG model consists of middleware and the

interaction among each component of middleware. TheMG middleware is responsible for managing andscheduling Grid resources to execute user tasks. The MGmiddleware consists of 5 sub components as depicted inFig. 3: service mediator, device monitor, replicationmanager, access manager and load scheduler.

First, mediator serves single interface for accessing toMG and performs like decision-making function unlikegeneral mediator. When the mobile device sends the loadlike ECG data or medical image to MG, they can send thedata without considering the interface. Also, the mediatormakes decision for next step of each job based on job type.Mediator supports abstraction and generalization forapplication over underlying data. Next, device monitorobserves the device for liveness. Liveness means that ausable communication path exists between the MG and

Fig. 3 Architecture of Mobile-to-Grid Gateway

3.3. Load Scheduler in Mobile-to-Grid Gateway

A. Load balancing between mobile devices and MGAs shown in Fig. 2, when the mobile device wants to

process their load on the grid resources, they shouldtransfer the load to mobile-to-grid gateway. In this pointof view, it is obvious that MG will experience a problemin terms of system over-load, that is, failure of single point[4]. To address this over-load problem, the MG whichperforms as a load balancer should have some functionssuch as scheduler to distribute each job evenly. In thispaper, we suppose that the number ofMG is two becausecalculating the number of MG for guaranteeing thereliability is out of scope.

3 of 7

Ubiquitous Mobile Network Static Grid Network

Fig. 4 Load Balancing between MG and mobile device

When a mobile device connects to MG, the MGcontrols the number of incoming connection between MGand device as depicted in Fig. 4. Namely, each gatewaytries to share the connection evenly. For example, suppose

that first MG has 10 connections with mobile device andsecond MG has 5 connections. If a new mobile deviceconnects to the first MG, the first MG let the mobiledevice change the connection point from the first one tothe second one. Therefore, the number of connectionbetween MG and device in each gateway is almost same

all the time.

B. Load Scheduling between MG and Grid ResourceAs shown in Fig. 5, the load scheduler consists of 4

components. First, Resource classifier sorts the resourcesbased on CPU and bandwidth. The task analyzer dividesthe jobs into indivisible job and divisible job based on jobtype. Indivisible job is the set of sub-processes (e.g.MPICH-G2 application) which has strong relation amongsub-processes such as very small delay time forexchanging messages, while the latter can be consideredas data set with no relation. Resource analyzer takes SLAand status information of Grid resources from GridInformation Service (GIS) [5]. After that, it generates anavailable resource set for processing medical data.

Load Scheduler

i~ffefi6iR Classifier R A nalyzer

Q111111,11Si-Xw

; +---------~~......................Schediuler

Divisible Job Scheduling Indivisible Job Scheduling

Fig. 5 Load Scheduler in MG

Generally, the random load balancing and weightedround robin load balancing do not consider the resourcerequirements such as well-known performance indexes e.g.network bandwidth and CPU. Actually, when job isassigned to resource, characteristic of job should be

considered for efficient processing. In this paper, we useCPU and bandwidth between MG and grid resource toreflect feature of job when task analyzer divides the jobinto 2 types and selects available resources. The schedulerconsists of two sub-phases: indivisible job scheduling anddivisible job scheduling. We design this algorithm toschedule divisible load first and proceed to distributeindivisible load as the second. In this case, the finish timeis the time spent for executing final indivisible task.

3.4. Load balancing scheme oftheMG

Now, we discuss more about scheduling scheme withgiven parameters as described in Table 1.

Table 1 Fundamental Symbol for SCL Scheduling Policy

Symbol DescriptionL

Set of load consisting of indivisible and divisibleL tasks. e.g. L = t ax, Ti2, Ti3, (a2 I T5s,........... (7an

Ti Set of indivisible tasksa, Load fraction allocated to processor Pi

Ratio of the time taken by processor Pi, towi compute a given load, to the time taken by a

standard processor, to compute the same load

T Time taken to process a unit load by the standardep processor

Ratio of the time taken by link, to communicate azi given load, to the time taken by a standard link, to

communicate the same loadTime taken to communicate a unit load on a

Tern standard linkThe total finish-time needed to execute all the

Si indivisible tasks on a single processore.g. S, t= + t2+ t3+ ... + ti, (i = n(T))

fti Thefinish-time of resources i

Phase 1. Divisible Job SchedulingThe basic idea underlying the process of scheduling

divisible loads to minimize the processing time is indevising efficient load distribution strategies. It isimportant to note that we are addressing the problem ofload partitioning in a heterogeneous system of processorsand links, and so dividing the load into equal sizedfractions will naturally results in a poor performance. Theproblem is then to choose the size of these load fractionsin such a way that our objective of minimum processingtime is met.

4 of 7

BEGIN:Step 1:

Divide divisible one Job into n sub-job{ ±ao+ a1±+ a2+±...±+a° }Select M resources for job processing from thegrid resource

Step 2:Get each CPU information of selected resourcesfrom the Grid Information Service (GIS)Get bandwidth information between gatewayand selected resources from the GIS

Step 3:Calculate the fraction of processing load a, foreach resource node by using following equation:aiwiTcp =aa1jwi+j,TP + ai+lzi+Tcm, for i = 0,1,.n-iDistribute loads to each resource

Step 4:If n(T)=0

fti = Final time for the last resource i toexecute task

exitElse go to Phase 2

ENDFig. 6 Divisible Job Scheduling Process ofMG

In the first step task analyzer divides load into sub-divisible load, and sum of entire fraction of job equals 1.And scheduler selects the M resources from the availableresource set for processing divisible job. In next step, thescheduler collects CPU and bandwidth information ofselected resources from the Grid Information Service(GIS) for calculating the size of load. In step 3, thescheduler Calculate the fraction of processing load a, foreach resource node by using collected resourceinformation and following equation:

a,iwvTc +aiziTcm = a+Iw+,Tcp + a1+1z1+Tcm,ao +a1 +a2 +...+an-1 =l and for i= 0,1,...,n -1

a,w,Tcp is the time to process the fraction a, of the entireload on the i-th processor. Likewise, azTcm is the time totransmit the fraction a, of the entire load over the i-th link.These equations can be solved recursively, and closed-form solutions are also possible. After choosing the size ofload for each resource, the load can be distributed to eachresource. Finally, if we do not have any indivisible tasks,the first phase ends in here. Otherwise, it goes to thesecond phase for divisible tasks.

Phase 2. Indivisible Job SchedulingThe second phase checks the number of indivisible

tasks is larger than the given resources M: if it is so, step1 skip to step 4 and just distribute all tasks to idleresources, otherwise it proceeds to step 2 for thedistribution of indivisible tasks as large as the number ofresources then go to step 3. In the step 3, from theremaining set of indivisible tasks, it tries to find any tasksuch that minimize gap between the average finish-timewhich means that needed time to complete imposed joband practical one on each resource iteratively. Then this

selected task first will be allocated to the most availableresource with respect to finish-time. Finally, if we do nothave any divisible tasks, the first phase ends in here.

BEGIN:Step 1:

Ifn(T) > Mwhich was selected for indivisible jobcontinueElse let T-,rs= Ti

Select resources from the available resourcesetGo to Step 4

Step 2:Schedule the n(T)=M toresources

the M available

Step 3:From Remaining Set {n(T)-M, n(T)-M-1,...,1of unscheduled indivisible taskselect any tasks and distribute them to resourcessatisfying following condition:

Min[D JS,1M-ftj ]forx =1,2,....Mrepeat Step 3 until all indivisible tasks aredistributed to resources

Step 4:fti = Final time for the last resource i toexecute taskexit

ENDFig. 7 Indivisible Job Scheduling Process ofMG

4. SIMULATION AND RESULTS

In this section, we want to discuss the performance ofproposed algorithm. First we evaluate imposed loads toeach MG to examine the degree of load sharing. Weassume that there are two MGs for receiving data frommobile deivces. Fig. 8 shows that differences of imposedload size between two MGs. In this case, two lines areparallel increasing. Averagely gaps might be 4,900 inevery points of horizontal axis. Because the concept ofload balancer with appropriate policies are not applied tothis simulation, the degree of load sharing is too low;obviously it means that a particular MGs will beoverloaded and more unstable than the others. Therefore,it might lead the failure of single point which is wellknown problem in the distributed computing environments.

Fig.u8uTheegreedonoflo,adohaiGdGamongwMG

35000

>1X600000

500

X 2 3 4 5 a 7 eThe Nulmber of Mkobile Deices

Fig. 8 The degree of load sharing among MGs

5 of 7

In order to address this problem, we proposed theresource scheduling policy as the dynamic load balancingpolicy in wireless Grid. For showing clearer effectivenessof scheduling policy between MG and device and extendscalability of mobile-to-Grid system, we set the number ofmobile devices to about 46 and 2 for MGs. And 100 unitload size is applied to this simulation.

Auffulathitve Load oni Mo-bilw to C~Gid Cato1 a

~ 4

(a)Unbalance ~dLord(wl. ,fitRLB) t Rr~r~

Accumulative t-oad on MAcbflI to, G3i d Gat,eway

The Numbe~r of Mobil Deie

(b)Balanced Load (with proposed algorithm)Fig. 9 Comparison of load balancing effect

First, Fig. 9 (a) shows that difference of loads handledby a MG get larger as increasing the number of mobiledevices. While a simulation result in Fig. 9 (b) representstotally different appearance. In summary, two lines go upwith small gaps of load. Though this simulation isrestricted because we suppose that two MGs arehomogeneous and in same conditions simply, we give theevidence of unbalanced situation as well as usefulness ofproposed load balancing strategy.Now we turn to discuss about loss rate, finish time and

efficiency in terms of finish time variation in schedulingscheme between MG and grid resources comparing toRandom load balancing (RLB) algorithm and weightedround robin load balancing (WRRLB) algorithm. Theeffective of load balancing algorithm can be measured bythree performance issues; loss rate, finish time, efficiency.The loss rate can be derived from an equation such that(incoming load -outgoing load)/incoming load (0 ). Andthe finish time of given tasks such as data, jobs is theperiod such that spent for last execution of job on aparticular resource node. In addition, we can define theefficiency as the quality of being able to do a task

successfully, without wasting time. To measure efficiency,we may use time variation of each node.

Thus to evaluate performance of load balancing schemeand get more reliable result, we compare the proposedscheduling scheme with RLB and WRRLB algorithmsrespectively. In case of RLB, it gives us lower boundary ofload balancing performance. And WRRLB is the well-performed load scheduling scheme in DistributedComputing Systems.

In Random algorithm, arbitrarily load might beallocated to resources and WRRLB gives load to more-capable resources. On the other hands, proposedscheduling policy tries to find optimal load balancing casefor given resources by seeking the minimum finish time.We assume that 20 resources are ready to work. Fig. 10shows the result of running simulation.

In case of finish time as shown in Fig. 10 (a), as offeredloads are increased, all algorithms show increasing line.However, the gradient of proposed algorithm is quitesmaller than other load scheduling algorithm. It means thatthe proposed algorithm can process offered loads moreefficiently. RLB and WRRLB show declined slope after20 x I O point. This appearance originates from thenumber of available resources. This appearance originatesfrom the number of available resources. Since a largechunk of loads is assign to Mobile-to-Grid Gateway, thedegree of load distribution might increase probabilistically.We finally obtain small variation of finish time inproposed algorithm even though large amounts of loadsimpose to resources. Therefore, we believe that optimalresult can give us approximate way toward globaloptimization.As offered load going up, the line representing loss rate

is continuously increasing in Fig. 10 (b). The occurrenceof loss rate might be caused by the lack of availableresources or wrong decision of load distribution.Especially, although there are still lots of resources whichhave capacity to handle load, RLB algorithm is blind.Hence we get very high loss rate at end of offered load.WRR offers us delicate strategy so that it tries to scheduleload into more-capable machines in advance. Namely,more-capable machine has a high priority to treat load. Inaddition, we obtain a quite good result from proposedscheduling scheme.When lO0x 1 O' loads which consist of divisible loads and

indivisible loads are offered to MG, we can get result asdepicted in Fig. 10 (C). This figure shows finish timevariation of each node. For representing variation we usenormalized standard deviation of finish time on each node.In RLB algorithm, variation on each resource isdramatically changed. In case of WRRLB, it is better thanRLB in terms of stability, but its variation is quite big.Though other resources complete the tasks early, some

6 of 7

resources take much longer time for processing their loadbecause both RLB and WRRLB do not consider theresource characteristics. In proposed scheme, processingtime is evenly distributed. Almost all resources can finishtheir job at the same time.

Finish Time vs Offered Load

4000035000 430000

20000*O 15000

I. 00005000

01 2 4

Offered LoadI(x10O)

R- LB --- WRRLB ......... Proposed Model

(a) Finish time ComparisonLoss Rate vs- Offered Load

40

35

30

25

20

10

.

10 20~~~~~30. 40..Offere. Load.. ...5

(b)LosrteComarsoLThc,ency

load balancing and guarantee the high system availabilitywith meaningful approaches. Especially, in order forreliable load sharing among the Grid resources, loadscheduler for indivisible job and indivisible job wasdiscussed and we showed that it meets the right finish time,low loss rate and high efficiency with proposed loadbalancing.

AcknowledgementThis research was supported by the MIC (Ministry ofInformation and Communication), Korea, under theITRC(Information Technology Research Center) supportprogram supervised by the IITA(Institute of InformationTechnology Assessment)

REFERENCES

[1] Junseok Hwang; Aravamudham, "Middleware servicesfor P2P computing in wireless Grid networks", InternetComputing, IEEE Volume 8, Issue 4, July-Aug. 2004[2] Gio Wiederhold, "Mediators in the Architecture ofFuture Information Systems", IEEE Computer society,Computer Volume 25, Issue 3, March 1992[3] Dantong Yu and Thomas G. Robertazzi, Divisible LoadScheduling for Grid Computing, Department of Physics,Brookhaven National Laboratory, Upton, NY 11973, USA[4] Beverly Yang, Hector Garcia-Molina, "Designing aSuper-Peer Network", p.49, 19th International Conferenceon Data Engineering (ICDE'03), 2003.[5] 1. Foster, et al., "The Grid: Blueprint for a FutureComputing Infrastructure", Morgan Kaufmann, 1999.

100- ' 2 3 5 6 7 8. 9 10 11 12 13 1l4 15 16e17 18 19 20Each niod

(c) Standard deviation of Processing time on each resourceFig. 10 Performance Comparison of the load scheduling

scheme

5. CONCLUSION

So far, we discussed the new feasible Mobile GridComputing model using the gateway concept forsupporting e-Health application and improving theefficiency of Grid services provided to mobile device. Wealso dealt with the efficient load scheduling mechanismfor processing divisible load and indivisible load.

In performance evaluation, we showed that proposedscheduling among mobile-to-Grid gateway can support the

7 of 7